Rough implementation of scratch

  only in the follow-up study can we find that this case is very unstructured and superficial, but it is very good as a learning example of the preliminary introduction of the strategy. In particular, let me have a general understanding of the specific framework of scratch.
  the url used in this example is as follows

url = "http://quote.eastmoney.com/stocklist.html"
url = "https://gupiao.baidu.com/stock/"

Steps:

1. Step 1: build the project and Spider template
2. Step 2: write Spider
3. Step 3: write ITEM Pipelines

Step 1

This step is the brainless building project

scrapy startproject BaiduStocks
cd BaiduStocks
scrapy genspider stocks baidu.com

Step 2

• Configure the stocks.py file
• Modify the processing of the returned page
• Modify the handling of new url crawling

# -*- coding: utf-8 -*-
import scrapy
import re


class StocksSpider(scrapy.Spider):
    name = "stocks"
    start_urls = ['http://quote.eastmoney.com/stocklist.html']

    def parse(self, response):
        for href in response.css('a::attr(href)').extract():
            try:
                stock = re.findall(r"[s][hz]\d{6}", href)[0]
                url = 'https://gupiao.baidu.com/stock/' + stock + '.html'
                yield scrapy.Request(url, callback=self.parse_stock)
            except:
                continue

    def parse_stock(self, response):
        infoDict = {}
        stockInfo = response.css('.stock-bets')
        name = stockInfo.css('.bets-name').extract()[0]
        keyList = stockInfo.css('dt').extract()
        valueList = stockInfo.css('dd').extract()
        for i in range(len(keyList)):
            key = re.findall(r'>.*</dt>', keyList[i])[0][1:-5]
            try:
                val = re.findall(r'\d+\.?.*</dd>', valueList[i])[0][0:-5]
            except:
                val = '--'
            infoDict[key] = val

        infoDict.update(
            {'Stock name': re.findall('\s.*\(', name)[0].split()[0] + \
                     re.findall('\>.*\<', name)[0][1:-1]})
        yield infoDict

Step 3: write Piplines

• Configure the pipelines.py file
• Define the processing class for the crawled item

#Pipeline.py
# -*- coding: utf-8 -*-

# Define your item pipelines here
#
# Don't forget to add your pipeline to the ITEM_PIPELINES setting
# See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html


class BaidustocksPipeline(object):
    def process_item(self, item, spider):
        return item


class BaidustocksInfoPipeline(object):
    def open_spider(self, spider):
        self.f = open('BaiduStockInfo.txt', 'w')

    def close_spider(self, spider):
        self.f.close()

    def process_item(self, item, spider):
        try:
            line = str(dict(item)) + '\n'
            self.f.write(line)
        except:
            pass
        return item

#setting.py
ITEM_PIPELINES = {
    'BaiduStocks.pipelines.BaidustocksInfoPipeline': 300,
}

Finally start the crawler

scrapy crawl stocks

Configure concurrent connection options

setting.py file
Concurrent menu requests: the maximum number of concurrent requests to download for Downloader, 32 by default
Current ITEM: ITEM pipeline maximum concurrent ITEM processing quantity default 100
Concurrent > requests > per > domain the maximum number of concurrent requests per target domain name, default 8
Concurrent > requests > per > IP the maximum number of concurrent requests per target IP, default 0, not 0 valid